Data Science and Machine Learning Articles | Yearly round-up 2019
What is Boosting in Ensemble Learning
Boosting helps to improve the accuracy of any given machine learning algorithm. It is algorithm independent so we can apply it with any learning algorithms. It is not used to reduce the model variance.
Boosting involves many sequential
iterations to strengthen the model accuracy, hence it becomes computationally costly.
What is Bagging in Ensemble Learning
Ensemble Learning says, if we can build multiple models then why to select the best one why not top 2, again why not top 3 and why not top 10. Then if you find top 10 deploy all 10 models. And when new data comes, make a prediction from all 10 models and combine the predictions and finally make a joint prediction. This is the key idea of ensemble learning.
How to create an Impressive Resume | Make it Simple
It does not matter how much subject knowledge you have, until you do not get a chance to show case it. It will be hidden somewhere within you. If you cannot market yourself well then you will always be lost in the crowd. So why am I talking about all these things. Imagine you are searching for a job and you have all the credentials required, however you are not getting shortlisted even. So what to do? Where is the problem? Are you marketing yourself well? Let’s discuss all these points in details.
How to start career in Data Science and Machine Learning
It does not matter how much experience you have, actually anybody can start or switch to data science and machine learning. The only important this is, how much eager you are for it. What it means to you. If you are very much keen to work in this field then nobody can stop you. There might be some short term hurdles however if you are focused enough and know your goals regarding where you want to see yourself after certain years, then you will definitely be successful in overcoming those hurdles.
Do you know AI can develop a “Sense of Smell” which can Detect Illnesses From Human Breath
Lot of research is being done in medical field, where researchers are working to develop AI models which can even develop the “Sense of smell”.It will help medical field to detect illness by smelling the human’s breath.They have achieved great success in detecting chemicals called aldehydes. Aldehydes are associated with human illnesses and stress.
Bayes’ Theorem with Example for Data Science Professionals
Bayes Theorem is the extension of Conditional probability. Conditional probability helps us to determine the probability of A given B, denoted by P(A|B). So Bayes’ theorem says if we know P(A|B) then we can determine P(B|A), given that P(A) and P(B) are known to us.
Conditional Probability with examples For Data Science
Conditional Probability helps Data Scientists to get better results from the given data set and for Machine Learning Engineers, it helps in building more accurate models for predictions.
Probability Basics for Data Science
Probability is used to predict the likelihood of the future event.
Statistics is used to analyse the past events
Also,
Probability tells us what will happen in a given ideal world?
While Statistics tells about how ideal is the world?
Probability is the basics of Inferential Statistics.
Variance, Standard Deviation and Other Measures of Variability and Spread
Variance and Standard Deviation are the most commonly used measures of variability and spread. Variability and spread are nothing but the process to know how much data is being varying from the mean point.
A Complete Guide to K-Nearest Neighbors Algorithm – KNN using Python
k-Nearest Neighbors or kNN algorithm is very easy and powerful Machine Learning algorithm. It can be used for both classification as well as regression that is predicting a continuous value. The very basic idea behind kNN is that it starts with finding out the k-nearest data points known as neighbors of the new data point for which we need to make the prediction. And then if it is regression then take the conditional mean of the neighbors y-value and that is the predicted value for new data point. If it is classification then it takes the mode (majority value) of the neighbors y value and that becomes the predicted class of the new data point.
Step by Step Approach to Principal Component Analysis using Python
Principal Component Analysis or PCA is used for dimensionality reduction of the large data set. Using PCA we can speed-up the ML algorithms by reducing the feature spaces.
A Complete Guide to Principal Component Analysis – PCA in Machine Learning
Principal Component Analysis or PCA is a widely used technique for dimensionality reduction of the large data set. Reducing the number of components or features costs some accuracy and on the other hand, it makes the large data set simpler, easy to explore and visualize. Also, it reduces the computational complexity of the model which makes machine learning algorithms run faster. It is always a question and debatable how much accuracy it is sacrificing to get less complex and reduced dimensions data set. we don’t have a fixed answer for this however we try to keep most of the variance while choosing the final set of components.
How to Use LinkedIn to Drive Traffic to Your Blog
Linkedin is a professional networking platform. where employers and employees can connect to each other. LinkedIn had 630 million registered members in 200 countries as
What is Logistic Regression?
Logistic regression is used for binary classification problem which has only two classes to predict. However with little extension and some human brain, it can easily be used for multi class classification problem. In this post I will be explaining about binary classification. I will also explain about the reason behind maximizing log likelihood function.
What is Multicollinearity?
Multicollinearity occurs in a multi linear model where we have more than one predictor variables. So Multicollinearity exist when we can linearly predict one predictor variable (note not the target variable) from other predictor variables with significant degree of accuracy. It means two or more predictor variables are highly correlated. But not the vice versa means if there is low correlation among predictors then also multicollinearity may exist.
What is stepAIC in R?
In R, stepAIC is one of the most commonly used search method for feature selection. We try to keep on minimizing the stepAIC value to come up with the final set of features. “stepAIC” does not necessarily means to improve the model performance, however it is used to simplify the model without impacting much on the performance. So AIC quantifies the amount of information loss due to this simplification. AIC stands for Akaike Information Criteria.
Feature Selection Techniques in Regression Model
Feature selection is a way to reduce the number of features and hence reduce the computational complexity of the model. Many times feature selection becomes very useful to overcome with overfitting problem. Feature selection helps us in determining the smallest set of features that are needed to predict the response variable with high accuracy. if we ask the model, does adding new features, necessarily increase the model performance significantly? if not then why to add those new features which are only going to increase model complexity.
Important Use Cases of NLP
NLP can organize unstructured data and perform several automated tasks such as automatic summarization, sentiments analysis, speech recognition, etc.
Basic Statistics for Data Science – Part 1
Types of Statistics: Descriptive vs Inferential
Basic terminology like Population vs Sample
Types of Variables: Numerical vs Categorical
Measures of central tendencies: Mean, Median and Mode and their specific use cases
Measures of dispersion/spread: Variance, standard deviation etc.
